The present invention, relates generally to computed tomography (CT) systems, and more particularly to a CT industrial inspection system and method for generating high-resolution CT images using pre-correction techniques.
In present date industrial inspection processes, different types of measurement systems are available such as CT, coordinate measuring machines (CMM) and laser-based profilometry. Each inspection modality has its own advantages and disadvantages associated therewith. Modalities such as CMM and laser-based profilometry can measure external surfaces with high accuracy, but cannot measure internal features unless the part is cut open. To date, CT is one of the more versatile of the measurement/inspection systems for revealing both the internal and external structures of industrial parts in a non-destructive manner. The potential industrial applications of CT include reverse engineering, rapid prototyping, quality assurance, casting simulation & validation, tire development, first article inspection, ceramic porosity inspection, process validation, parts qualification and defect detection, to name a few. However, improved inspection accuracy of industrial CT is desirable, for widespread applications thereof.
For example in the area of reverse engineering, CT has not been optimized for capturing detailed external surface features, which can be crucial for capturing the design intent. The factors affecting CT accuracy in this regard include (among other aspects) beam-hardening, partial volume effect, scattering and off-focal radiation. Thus, in order to improve CT inspection accuracy, more effective methods are needed for removing the effects of these artifacts. In the area of CT image reconstruction, filtered backprojection (FBP) is a common technique because of its fast computation and ease of implementation. However, because FBP oversimplifies the CT data acquisition into an ideal Radon transform (i.e., Fan Beam transform, cone beam transform or any other transform depending on the particular acquisition geometry), the reconstructed image suffers from artifacts such as beam hardening and partial volume as discussed above.
In order to improve image quality, iterative reconstruction techniques have been employed to correct system imperfections such as focal spot size, detector point spread (PSF) function, detector time lag, non-linear partial volume error, scatter, beam hardening etc. Iterative reconstruction techniques are based on different mathematical principles, such as the statistical approach of maximum likelihood, and the least squares approach, for example. These techniques permit the incorporation of a dedicated forward model of the data acquisition. Typically, in an iterative reconstruction approach, the reconstructed image is incrementally updated using the differences between the initial projection measurements and the forward projection model.
Although iterative reconstruction techniques significantly improve image quality, the computational complexity associated with iterative reconstruction is highly intensive, as these techniques require multiple applications of computationally expensive forward and backprojections. Hence, iterative methods are not yet widely used in CT. Accordingly, it is desirable to be able to provide a technique for capturing both internal and external features of an object to be inspected and a technique that improves the image quality of reconstructed images without a significant increase in computation time.